import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class UpperCAmelCase		(   __A   ,			__A   ,			__A   ,			unittest.TestCase    ):


				'''simple docstring'''






				lowerCamelCase_        =   StableDiffusionInstructPixaPixPipeline
				lowerCamelCase_        =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
				lowerCamelCase_        =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowerCamelCase_        =   IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowerCamelCase_        =   IMAGE_TO_IMAGE_IMAGE_PARAMS
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						torch.manual_seed(0   )
						A_						:							List[str]      = UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)						,      layers_per_block=2						,      sample_size=3_2						,      in_channels=8						,      out_channels=4						,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,      cross_attention_dim=3_2						,      )
						A_						:							List[str]      = PNDMScheduler(skip_prk_steps=lowercase   )
						torch.manual_seed(0   )
						A_						:							List[Any]      = AutoencoderKL(
						    block_out_channels=[3_2, 6_4]						,      in_channels=3						,      out_channels=3						,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,      latent_channels=4						,      )
						torch.manual_seed(0   )
						A_						:							Any      = CLIPTextConfig(
						    bos_token_id=0						,      eos_token_id=2						,      hidden_size=3_2						,      intermediate_size=3_7						,      layer_norm_eps=1E-05						,      num_attention_heads=4						,      num_hidden_layers=5						,      pad_token_id=1						,      vocab_size=1_0_0_0						,      )
						A_						:							Tuple      = CLIPTextModel(lowercase   )
						A_						:							Any      = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

						A_						:							Dict      = {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase=0   ):



						"""simple docstring"""





						A_						:							int      = floats_tensor((1, 3, 3_2, 3_2)						,      rng=random.Random(lowercase   )   ).to(lowercase   )
						A_						:							Any      = image.cpu().permute(0						,      2						,      3						,      1   )[0]
						A_						:							List[str]      = Image.fromarray(np.uinta(lowercase   )   ).convert('RGB'   )
						if str(lowercase   ).startswith('mps'   ):
								A_						:							List[Any]      = torch.manual_seed(lowercase   )
						else:
								A_						:							Optional[int]      = torch.Generator(device=lowercase   ).manual_seed(lowercase   )
						A_						:							Tuple      = {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'image_guidance_scale': 1,
						    'output_type': 'numpy',
						}
						return inputs
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Optional[int]      = 'cpu'  # ensure determinism for the device-dependent torch.Generator
						A_						:							Tuple      = self.get_dummy_components()
						A_						:							Tuple      = StableDiffusionInstructPixaPixPipeline(**lowercase   )
						A_						:							Optional[int]      = sd_pipe.to(lowercase   )
						sd_pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							List[str]      = self.get_dummy_inputs(lowercase   )
						A_						:							Optional[Any]      = sd_pipe(**lowercase   ).images
						A_						:							int      = image[0, -3:, -3:, -1]
						assert image.shape == (1, 3_2, 3_2, 3)
						A_						:							Optional[Any]      = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Optional[Any]      = 'cpu'  # ensure determinism for the device-dependent torch.Generator
						A_						:							Any      = self.get_dummy_components()
						A_						:							Optional[Any]      = StableDiffusionInstructPixaPixPipeline(**lowercase   )
						A_						:							int      = sd_pipe.to(lowercase   )
						sd_pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							Dict      = self.get_dummy_inputs(lowercase   )
						A_						:							Any      = 'french fries'
						A_						:							Any      = sd_pipe(**lowercase						,      negative_prompt=lowercase   )
						A_						:							Optional[int]      = output.images
						A_						:							Tuple      = image[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						A_						:							Dict      = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							List[str]      = 'cpu'  # ensure determinism for the device-dependent torch.Generator
						A_						:							Any      = self.get_dummy_components()
						A_						:							int      = StableDiffusionInstructPixaPixPipeline(**lowercase   )
						A_						:							int      = sd_pipe.to(lowercase   )
						sd_pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							Optional[Any]      = self.get_dummy_inputs(lowercase   )
						A_						:							Any      = [inputs['prompt']] * 2

						A_						:							Any      = np.array(inputs['image']   ).astype(np.floataa   ) / 255.0
						A_						:							Union[str, Any]      = torch.from_numpy(lowercase   ).unsqueeze(0   ).to(lowercase   )
						A_						:							Union[str, Any]      = image / 2 + 0.5
						A_						:							Optional[int]      = image.permute(0						,      3						,      1						,      2   )
						A_						:							Any      = image.repeat(2						,      1						,      1						,      1   )

						A_						:							Optional[int]      = sd_pipe(**lowercase   ).images
						A_						:							Any      = image[-1, -3:, -3:, -1]

						assert image.shape == (2, 3_2, 3_2, 3)
						A_						:							Union[str, Any]      = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Optional[Any]      = 'cpu'  # ensure determinism for the device-dependent torch.Generator
						A_						:							List[str]      = self.get_dummy_components()
						A_						:							List[str]      = EulerAncestralDiscreteScheduler(
						    beta_start=0.0_0085						,      beta_end=0.012						,      beta_schedule='scaled_linear'   )
						A_						:							Tuple      = StableDiffusionInstructPixaPixPipeline(**lowercase   )
						A_						:							Optional[int]      = sd_pipe.to(lowercase   )
						sd_pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							int      = self.get_dummy_inputs(lowercase   )
						A_						:							str      = sd_pipe(**lowercase   ).images
						A_						:							int      = image[0, -3:, -3:, -1]

						A_						:							List[Any]      = [round(lowercase						,      4   ) for x in image_slice.flatten().tolist()]
						print(','.join([str(lowercase   ) for x in slice]   )   )

						assert image.shape == (1, 3_2, 3_2, 3)
						A_						:							str      = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Dict      = self.get_dummy_components()
						A_						:							Optional[int]      = StableDiffusionInstructPixaPixPipeline(**lowercase   )
						A_						:							Tuple      = VaeImageProcessor(do_resize=lowercase						,      do_normalize=lowercase   )
						A_						:							Tuple      = pipe.to(lowercase   )
						pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							List[Any]      = pipe(**self.get_dummy_inputs_by_type(lowercase						,      input_image_type='pt'   )   )[0]

						A_						:							Optional[Any]      = components['vae']
						A_						:							Tuple      = self.get_dummy_inputs_by_type(lowercase						,      input_image_type='pt'   )

						for image_param in self.image_latents_params:
								if image_param in inputs.keys():
										A_						:							Tuple      = vae.encode(inputs[image_param]   ).latent_dist.mode()

						A_						:							Optional[int]      = pipe(**lowercase   )[0]

						A_						:							Any      = np.abs(out - out_latents_inputs   ).max()
						self.assertLess(lowercase						,      1E-4						,      'passing latents as image input generate different result from passing image'   )





@slow
@require_torch_gpu
class UpperCAmelCase		(   unittest.TestCase    ):


				'''simple docstring'''






				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
				def     lowerCAmelCase_  (		self						,      lowercase=0   ):



						"""simple docstring"""





						A_						:							Any      = torch.manual_seed(lowercase   )
						A_						:							Dict      = load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'   )
						A_						:							List[str]      = {
						    'prompt': 'turn him into a cyborg',
						    'image': image,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 7.5,
						    'image_guidance_scale': 1.0,
						    'output_type': 'numpy',
						}
						return inputs
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Union[str, Any]      = StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,      safety_checker=lowercase   )
						pipe.to(lowercase   )
						pipe.set_progress_bar_config(disable=lowercase   )
						pipe.enable_attention_slicing()

						A_						:							Optional[Any]      = self.get_inputs()
						A_						:							Optional[Any]      = pipe(**lowercase   ).images
						A_						:							Any      = image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							str      = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]   )

						assert np.abs(expected_slice - image_slice   ).max() < 1E-3
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							str      = StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,      safety_checker=lowercase   )
						A_						:							List[Any]      = LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
						pipe.to(lowercase   )
						pipe.set_progress_bar_config(disable=lowercase   )
						pipe.enable_attention_slicing()

						A_						:							str      = self.get_inputs()
						A_						:							List[str]      = pipe(**lowercase   ).images
						A_						:							List[Any]      = image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							Optional[Any]      = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]   )

						assert np.abs(expected_slice - image_slice   ).max() < 1E-3
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Tuple      = StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,      safety_checker=lowercase   )
						A_						:							Dict      = DDIMScheduler.from_config(pipe.scheduler.config   )
						pipe.to(lowercase   )
						pipe.set_progress_bar_config(disable=lowercase   )
						pipe.enable_attention_slicing()

						A_						:							Union[str, Any]      = self.get_inputs()
						A_						:							Tuple      = pipe(**lowercase   ).images
						A_						:							List[str]      = image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							Any      = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]   )

						assert np.abs(expected_slice - image_slice   ).max() < 1E-3
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Any      = 0

						def callback_fn(lowercase						,      lowercase						,      lowercase   ) -> None:
								A_						:							List[Any]      = True
								nonlocal number_of_steps
								number_of_steps += 1
								if step == 1:
										A_						:							Any      = latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 6_4, 6_4)
										A_						:							Any      = latents[0, -3:, -3:, -1]
										A_						:							Dict      = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]   )

										assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
								elif step == 2:
										A_						:							Optional[Any]      = latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 6_4, 6_4)
										A_						:							Tuple      = latents[0, -3:, -3:, -1]
										A_						:							Union[str, Any]      = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]   )

										assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2

						A_						:							List[Any]      = False

						A_						:							List[str]      = StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,      safety_checker=lowercase						,      torch_dtype=torch.floataa   )
						A_						:							Tuple      = pipe.to(lowercase   )
						pipe.set_progress_bar_config(disable=lowercase   )
						pipe.enable_attention_slicing()

						A_						:							str      = self.get_inputs()
						pipe(**lowercase						,      callback=lowercase						,      callback_steps=1   )
						assert callback_fn.has_been_called
						assert number_of_steps == 3
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						A_						:							Union[str, Any]      = StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,      safety_checker=lowercase						,      torch_dtype=torch.floataa   )
						A_						:							Optional[Any]      = pipe.to(lowercase   )
						pipe.set_progress_bar_config(disable=lowercase   )
						pipe.enable_attention_slicing(1   )
						pipe.enable_sequential_cpu_offload()

						A_						:							List[Any]      = self.get_inputs()
						A_						:							Any      = pipe(**lowercase   )

						A_						:							Dict      = torch.cuda.max_memory_allocated()
						# make sure that less than 2.2 GB is allocated
						assert mem_bytes < 2.2 * 1_0**9







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Optional[Any]      = self.get_inputs()
						# resize to resolution that is divisible by 8 but not 16 or 32
						A_						:							List[str]      = inputs['image'].resize((5_0_4, 5_0_4)   )

						A_						:							Any      = 'timbrooks/instruct-pix2pix'
						A_						:							Any      = StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    lowercase						,      safety_checker=lowercase						,      )
						pipe.to(lowercase   )
						pipe.set_progress_bar_config(disable=lowercase   )
						pipe.enable_attention_slicing()

						A_						:							int      = pipe(**lowercase   )
						A_						:							Any      = output.images[0]

						A_						:							int      = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert image.shape == (5_0_4, 5_0_4, 3)
						A_						:							Any      = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5E-3







def 							UpperCamelCase		(			__lowercase		:					Optional[Any]		,__lowercase		:					List[Any]		,__lowercase		:					Tuple      ):
		'''simple docstring'''




		if n == 0:
				return 1

		elif n % 2 == 1:
				return (binary_exponentiation(__lowercase		,n - 1		,__lowercase      ) * a) % mod

		else:
				A_						:							str      = binary_exponentiation(__lowercase		,n / 2		,__lowercase      )
				return (b * b) % mod


# a prime number
_UpperCAmelCase          =						701

_UpperCAmelCase          =						1000000000
_UpperCAmelCase          =						10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)


print((a / b) % p == (a * b ** (p - 2)) % p)






